The generation of three-dimensional image data sets from two-dimensional projection images captured using different projection directions is already widely known and forms a basis for three-dimensional tomographic methods in computed tomography (CT). It has however also been proposed with increasing frequency in the context of interventions, in particular minimally invasive interventions, to utilize the advantages of available three-dimensional information, with the result that for example, CT-like methods have also been proposed for the generation of three-dimensional image data sets for X-ray devices having a C-arm which can be employed at a point of intervention, for example known under the name “DynaCT”. In this situation, when the C-arm is rotated around the patient, projection images of the region of interest are captured using different projection directions, in other words different projection angles, from which projection images a three-dimensional reconstructed image data set can then be determined by means of known methods, for example analytical methods such as filtered back projection or iterative methods.
Problems occur with regard to such investigations for example in the situation when a supporting apparatus, in particular a stereotactic frame, is employed in the region of the neurosurgery. In this situation, the more general term of neurosurgical apparatus is intended to be understood in this description not only as a stereotactic frame in itself but in fact also to include other positioning aids, neurosurgical instruments, for example puncture needles, even also markers arranged for the most part on the patient, the instrument and/or the stereotactic frame, which are used for the registration of coordinate systems, for example by way of an optical tracking system. Stereotactic frames and/or other special apparatuses are used for guidance and execution in the case of a minimally invasive intervention, for example a puncture.
Should it then be intended to further support the intervention through the capture of a three-dimensional image data set, the neurosurgical apparatuses are situated completely or at least partially in the field of view of the X-ray device, in particular of an X-ray device having a C-arm. Because in particular frames and markers for the optical registration frequently consist of a very dense material, in particular exhibiting a high atomic number, they can cause artifacts in the image data sets. Although neurosurgical apparatuses which are improved in this respect, for example consisting of materials having a low atomic number, are also known, for example stereotactic frames made of carbon, these are however for the most part extremely expensive.
The typical workflow involved in capturing the image data set during a neurosurgical intervention is that the patient is positioned on a patient table and the neurosurgical apparatuses, in particular a stereotactic frame, are adjusted and fixed. Then the projection images are captured, for example during one rotation of the C-arm around the head of the patient. The captured projection images can for example be reconstructed using the Feldkamp algorithm to form the three-dimensional reconstructed image data set. The 3-D volume produced contains artifacts due to the neurosurgical apparatuses.
In order to eliminate these artifacts it is known to use metal artifact correction algorithms which in regions of strong attenuations, for example caused by metals, replace the image data with in particular linearly interpolated image data outside these regions. The use of beam hardening correction algorithms of an iterative nature has also already been proposed.
These approaches have the disadvantage that they are for the most part not suitable for handling effects of truncated projection images which in particular do not show the entire neurosurgical apparatus. Artifacts may remain or even be exacerbated. Furthermore, these algorithms depend on the quality of the projections and the segmentation, in particular the segmentation of regions to be interpolated because the segmentation is performed in the three-dimensional volume, in other words the uncorrected image data set. The algorithms have a strong noise dependence and can only be employed scarcely meaningfully in the case of low 3-D image quality since they operate in image-based fashion. A further disadvantage is the fact that the interpolation, in particular a linear interpolation, has too great an influence on the resolution and the image quality.